Bottom Line:
After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain.The significant correlations were distributed mainly to motor association areas for both real and imagined movements.These regions largely overlapped with brain regions that had significant connectivity to M1.

Affiliation: Department of Neurosurgery, Osaka University Medical School Suita, Japan.

ABSTRACTBrain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain-machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.

Figure 1: Task design and analysis procedure. (A) Task design. Participants performed a real movement task and an imagined movement task following the same task paradigm. Each trial consisted of four phases: a rest phase, an instruction phase, a preparation phase, and an execution phase. In the rest phase, participants fixed their eyes on a black fixation cross “+” presented for 4 s. A Japanese word representing one of three movements was then presented for 1 s during the instruction phase. Then, two timing cues, “> <” and “> <,” were presented during the preparation phase to enable the participants to prepare the execution of real or imagined movements. Finally, the participants performed the movement or imagined performing the movement presented during the instruction phase. Each of the three movements was performed 60 times. (B) Analysis procedure. The beam forming approach was used to extract 40 virtual channels from the left M1, and decoding accuracy was calculated using these channels. Seed-based functional connectivity of activities within the alpha band between M1 virtual channels and target voxels over the rest of the whole brain was calculated using imaginary coherence (IC) in individual participants. Then, the correlation coefficient between decoding accuracy and IC was calculated over the participants.

Mentions:
The experimental paradigm is shown in Figure 1A. We prepared two tasks: a real movement task and an imagined movement task. We have previously shown the contribution of M1 signals in classifying movement types using these motor tasks based on ECoG (Yanagisawa et al., 2009) and MEG (Sugata et al., 2012b). An epoch started with a 4-s rest phase, and a black fixation cross (+) was presented to fix the participant’s eyes on the screen. Then, a Japanese word representing one of the three right upper limb movements (grasping, pinching, or elbow flexion) was presented for 1 s to instruct the participant which movement to perform or imagine after the appearance of the execution cue. Two timing cues, “> <” and “> <,” were then sequentially presented for 1 s each to enable the participants to prepare the execution of the real or imagined movements. In the real movement task, the participants were instructed to perform the instructed movement presented on the display immediately after the appearance of the execution cue (×). In the imagined movement task, the participants were instructed to imagine performing the movement immediately after the appearance of the execution cue. Each of the three types of movements was performed 60 times during the real movement trials, and the movement in any given epoch was selected randomly. Then the imagined movement trials were conducted in the same manner.

Figure 1: Task design and analysis procedure. (A) Task design. Participants performed a real movement task and an imagined movement task following the same task paradigm. Each trial consisted of four phases: a rest phase, an instruction phase, a preparation phase, and an execution phase. In the rest phase, participants fixed their eyes on a black fixation cross “+” presented for 4 s. A Japanese word representing one of three movements was then presented for 1 s during the instruction phase. Then, two timing cues, “> <” and “> <,” were presented during the preparation phase to enable the participants to prepare the execution of real or imagined movements. Finally, the participants performed the movement or imagined performing the movement presented during the instruction phase. Each of the three movements was performed 60 times. (B) Analysis procedure. The beam forming approach was used to extract 40 virtual channels from the left M1, and decoding accuracy was calculated using these channels. Seed-based functional connectivity of activities within the alpha band between M1 virtual channels and target voxels over the rest of the whole brain was calculated using imaginary coherence (IC) in individual participants. Then, the correlation coefficient between decoding accuracy and IC was calculated over the participants.

Mentions:
The experimental paradigm is shown in Figure 1A. We prepared two tasks: a real movement task and an imagined movement task. We have previously shown the contribution of M1 signals in classifying movement types using these motor tasks based on ECoG (Yanagisawa et al., 2009) and MEG (Sugata et al., 2012b). An epoch started with a 4-s rest phase, and a black fixation cross (+) was presented to fix the participant’s eyes on the screen. Then, a Japanese word representing one of the three right upper limb movements (grasping, pinching, or elbow flexion) was presented for 1 s to instruct the participant which movement to perform or imagine after the appearance of the execution cue. Two timing cues, “> <” and “> <,” were then sequentially presented for 1 s each to enable the participants to prepare the execution of the real or imagined movements. In the real movement task, the participants were instructed to perform the instructed movement presented on the display immediately after the appearance of the execution cue (×). In the imagined movement task, the participants were instructed to imagine performing the movement immediately after the appearance of the execution cue. Each of the three types of movements was performed 60 times during the real movement trials, and the movement in any given epoch was selected randomly. Then the imagined movement trials were conducted in the same manner.

Bottom Line:
After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain.The significant correlations were distributed mainly to motor association areas for both real and imagined movements.These regions largely overlapped with brain regions that had significant connectivity to M1.

Affiliation:
Department of Neurosurgery, Osaka University Medical School Suita, Japan.

ABSTRACTBrain signals recorded from the primary motor cortex (M1) are known to serve a significant role in coding the information brain-machine interfaces (BMIs) need to perform real and imagined movements, and also to form several functional networks with motor association areas. However, whether functional networks between M1 and other brain regions, such as these motor association areas, are related to the performance of BMIs is unclear. To examine the relationship between functional connectivity and performance of BMIs, we analyzed the correlation coefficient between performance of neural decoding and functional connectivity over the whole brain using magnetoencephalography. Ten healthy participants were instructed to execute or imagine three simple right upper limb movements. To decode the movement type, we extracted 40 virtual channels in the left M1 via the beam forming approach, and used them as a decoding feature. In addition, seed-based functional connectivities of activities in the alpha band during real and imagined movements were calculated using imaginary coherence. Seed voxels were set as the same virtual channels in M1. After calculating the imaginary coherence in individuals, the correlation coefficient between decoding accuracy and strength of imaginary coherence was calculated over the whole brain. The significant correlations were distributed mainly to motor association areas for both real and imagined movements. These regions largely overlapped with brain regions that had significant connectivity to M1. Our results suggest that use of the strength of functional connectivity between M1 and motor association areas has the potential to improve the performance of BMIs to perform real and imagined movements.